1 General information

Courses take place from 9.15-12 and from 13.15-16 and in in room 136 if not otherwise mentioned
Carefully study the module description
Here you find a list of class members and contact information and groups.

1.1 Welcome Email

Dear students,

A warm welcome to the module Data skills for social work professionals!

As the first Monday of our class falls away (Pfingsten), we would like you to complete a few preparation tasks before the first meeting on Tuesday 21st.

Enroll on the moodle page (https://moodle.bfh.ch/course/view.php?id=37097) with the following key: FS24-bsc.
It is essential that you have R and R-Studio installed and running on your computer before the first classroom session. Please follow the instructions in the “Installation of R and R-Studio” guide (https://drive.switch.ch/index.php/s/ktNsnWxwkJ3olWG ), and if necessary, refer to the linked instructions on YouTube. If you have any questions, please feel free to contact us via email.
Familiarize yourself with R. We want you to take the opportunity of new AI tools and ask Copilot to take you through a tour in R (https://www.bing.com/chat?q=Microsoft+Copilot&FORM=hpcodx). Instruct Copilot on its task with the text below.
Finally, we invite you to familiarize yourself with the topic of “Data Science” and its application in social work. Create a forum post https://moodle.bfh.ch/mod/forum/view.php?id=2165224 , where you provide a concrete example of how data science can help improve the effectiveness of social work or promote the well-being of clients. What are the potential benefits and challenges of applying data science in this field? We look forward to reading your perspectives and ideas on this topic.

We wish you a successful preparation period and look forward to meeting you in person soon. Please let us know should you have any questions.

Kind regards

Dorian Kessler, Samin Sepahniya

Text to enter into Co-Pilot ein (Microsoft Copilot in Bing; important: verwenden Sie den Unterhaltungsstil «im höheren Masse kreativ/creative mode» (Schaltfläche in der Mitte des Bildschirms)):

Als Studierende(r) der Sozialen Arbeit möchte ich die Grundlagen der Programmiersprache R lernen, um statistische Datenanalysen für Projekte in der Sozialen Arbeit durchführen zu können. Ich habe keine Vorkenntnisse in Statistik oder Programmierung. Kannst du mir bitte eine schrittweise Einführung geben? Bitte beginne mit der Frage ob ich R und Rstudio installiert habe und wenn nein, unterstütze mich bei der Installation von R und RStudio. Zeige mir dann die grundlegenden Befehle und Funktionen von R. Ich würde ich gerne lernen, wie man einfache Datenanalysen durchführt (mit dplyr), Daten visualisiert (mit ggplot2) und Ergebnisse interpretiert. Folgende Dinge sind zu beachten:

Wähle ein schrittweises Vorgehen. Erzähle mir erst von dem nächsten Schritt, wenn ein Schritt abgeschlossen ist. Frage nach jedem Schritt nach, ob ich diesen erfolgreich abschliessen konnte, um sicherzustellen, dass ich alles richtig gemacht habe.
Sage mir als ersten Schritt genau wie ich mich visuell in RStudio orientieren kann und wo ich Eingaben machen muss. Wo befindet sich die Konsole/Skript/Datenübersicht/Dateienübersicht in RStudio?
Erkläre mir, was die Konsole ist und was ein R-Skript ist, wie man ein R-Skript erstellt und abspeichert und was der Zweck von Skripten ist. Arbeite mit mir mit einem R-Skript und sage mir, wie ich Befehle ausführen kann.
Bitte führe mich durch praktische Übungen und gebe mir Aufgaben, um das Gelernte zu festigen.
Biete mir Unterstützung bei Unklarheiten.
Arbeite mit Beispielen, welche für die Soziale Arbeit relevant sind. Erfinde relevante Daten aus den Bereichen Sozialhilfe oder Kindes- und Erwachsenenschutz.
Kommentiere den Code Zeile-für-Zeile detailliert aus, so dass ich ihn genau verstehe.
Biete mir am Schluss weitere Übungen an, falls ich Lust habe. Mache Vorschläge für Übungen.
Du bist eine R-Expert:in, weisst aber auch, dass angehende Sozialarbeiter:in in Sachen Programmierung wenig Wissen haben und das nicht technische Begriffe eine alltagssprachliche Erklärung benötigen.
Danke für deine motivierte Unterstützung und Hilfsbereitschaft! Du hilfst mir R zu lernen und dieses Wissen für Klient:innen einzusetzen.
Wichtige Details:
Bitte lasse das «print()» weg, falls nicht nötig.
Ergänze bei Strg jeweils Ctrl, falls gewisse Personen englische Windows Tastaturen haben.

2 General Introduction

2.1 Learning Goals

People learn basic data science tools.
People learn how to integrate data science in social work problem solving.
People learn how to do data science with R.

2.2 What is data science?

Term that emerged ca. 10 years ago. Predecessors: Statistics, Data analysis.
The science of creating valuable information from data
Practice-oriented science
Combines technical and field expertise

2.4 Datafication or why data science is becoming more important in the future

Data is the new oil.
Data contains information on human behavior = helps us better understand the human world and solve human problems.
In the era of AI, “data literacy” becomes a key skill in all areas of life, including social work –> it should be a basic competence
- Data awareness
- Skills to interpret and analyze data

2.6 Excercise

Think of a social work field
What is the goal of social work in that field: What aspects of your clients lives do you want to improve?
What existing data could you use to measure these aspects of your clients’ lives? Who owns the data? What specific information would you use to measure this? What are technical and ethical limitations?
Post your answers on this padlet

2.7 Kompetenznachweis

You will analyze one of the following data sets
Structure
- Einleitung: Vorstellung der Fragestellung und ihrer Relevanz für die Soziale Arbeit
- Methodik: Dokumentation dessen, welche Daten verwendet und wie sie ausgewertet wurden
- Resultate: Präsentation der Resultate
- Schlussteil: Diskussion und Interpretation der Resultate mit Bezug zum Gegenstand und Auftrag der Sozialen Arbeit
Die Studierenden liefern zudem ein R-Code File mit, in welchem die Aufbereitungs- und Auswertungsschritte festgehalten sind. Das Code-File muss reproduzierbar sein und die verwendeten Resultate herstellen.
Der Kompetenznachweis (Dokumentation, R-Code) wird in Gruppen von 2-3 Personen verfasst, verfügt jedoch über individuell verantwortete Teile im Text oder im Codefile (z.B. im Text: Einleitung, Methodik, Resultate, Schlussteil; im Code: Aufbereitung und Auswertung). Die individuellen Beiträge sind am Ende der Dokumentation als solche auszuweisen (Angabe der Kapitel; für Code: Angabe der Zeilennummern).

3 Introduction to R

3.1 General Information about R

R is free and open source.
R has excellent online documentation.
R has a very active user community (forums, blogs, etc.).
R is more than just statistical software.
R has interfaces to numerous other programs.
R is interdisciplinary.
R is gaining in importance and popularity! (see Popularity Statistics)
With RStudio, there is now a powerful tool for an easy and efficient workflow.
Advantages
- Free of charge
- Active community
- New methods are implemented faster
- More flexible/customizable: more than just statistical software
- Everyone can contribute
Disadvantages
- No centralized support
- Somewhat more difficult to learn (uses command line inputs instead of pull-down menus)
- Less consistency between procedures
- Anyone can contribute
Which organizations are behind R?
- General R information: https://www.r-project.org/about.html
- R source code contributors: https://www.r-project.org/contributors.html
- Comprehensive R Archive Network (CRAN) download and install R: https://cran.r-project.org/
- About RStudio: https://www.rstudio.com/about/
- Download RStudio: https://posit.co/downloads/
RStudio Environment
- Console Window
- Source Editor (Syntax window)
- File Window, Plot Window
- Environment Window, History Window
First steps
- R Studio Environment
- Working Directory
- R Base Package
- Install & load packages
- Simple calculations
- Data import
- Execute basic functions such as summary(), help()
- Comment codes with #

3.2 Working Directory, Objects, and Workspace

Working Directory
- getwd(): Displays the working directory.
- setwd(): Defines a new working directory.
- dir(): Displays the contents of the current working directory.
- Do not use \ for path specifications, use / or \\ instead.
Objects
- “Naming guidelines”: meaningful names; no spaces; start with a letter.
- a <- 10 or a = 10: Creates or overwrites the object a with the content on the right (10).
- a: Displays the content of the object a.
- rm(): Deletes objects from the workspace.
- save(a, b, file = "example.RData"): Saves the specified objects (a, b) in the current working directory.
- load("example.RData"): Loads all objects saved in the specified file.
- #: Starts a commented line that is not interpreted.
Workspace
- Here are all the objects that have been worked with.
- If the workspace was saved when closing (R asks for it), it will be loaded again when R starts.
- ls(): Displays all objects in the current workspace.
- The workspace is saved in the current working directory by default.

3.3 Example: Working Directory, Objects, and Workspace

# Comments start with #
# Everything in the line after # is ignored by R
5+5

getwd() # Display working directory
# Define working directory
# setwd("C:/some/path/") 

dir() # Display working directory

a <- 50 # Creates object a (number vector of length 1) with the single value 50
a

# With c() - concatenate you can also build a number vector with several elements:
b <- c(1, 2, 3, 4)

# or shorter
b <- seq(1,4)

# or even shorter
b <- 1:4

# Create object containing the first names of the Beatles
the.beatles <- c("John", "Paul", "George", "Ringo") 
the.beatles # compared to a, the object is now a string/character


# Object names must not have spaces. It is also recommended - and _ should be avoided (Google R Style Guide)
# Names should be meaningful. Naming should be consistent throughout the code file (dots, upper/lower case)

ls() # Display workspace
rm(die.beatles) # Delete object

4 Packages

Many objects (functions, data records, etc.) are located in so-called packages (see here)
Packages are written and maintained by countless voluntary authors. As a result, numerous methodological niches are well covered (especially in comparison to other statistical software packages)
Some packages are part of the core scope of R and are loaded by default when R is started. Other packages can be loaded on request.
Warning: Objects contained in packages (e.g. functions) may overlap by name (“function is masked”)
Functions in connection with packages:
- library(packagename): load installed package
- library(): display all installed packages
- library(help=packagename): Some package information
- search(): show currently loaded packages
- detach("package:packagename"): “unload” package again
- ls("package:packagename"): show all objects within a package
- packagename::bar: load a single object from a package instead of the whole package
- install.packages("packagename"): install package
- remove.packages("packagename"): uninstall package

4.1 Example: Packages and help function

# Let's assume we want to read in an SPSS file:
??spss

# provides references to the functions read.spss and read_por on the packages foreign and haven 
# Install package
# install.packages("foreign") 

# Only works if the package is also loaded
library(foreign)
?read.spss

# But general recommendation: Google or AI now usually provide better results than the help function

4.2 Exercise: Install and load the following packages.

dplyr: for working with data sets / data cleansing
ggplot2: for data visualization
haven: facilitates the reading of SPSS, SAS and Stata files in R.
readxl: Excel files (.xls and .xlsx) read into R
officer: enables the creation and editing of Microsoft Word documents (.docx) directly in R

5 Arithmetic and logical operators

Arithmetic operators
- + - * / ^
Comparison operators (logical operators)
- & | == != > < >= <=
Mathematical functions (see the help ?function in each case)
- exp(x)=e^x log(x) log10(x) sin(x) cos(x) tan(x)
- abs(x) sqrt(x) ceiling(x) floor(x) trunc(x) round(x, digits=n)

5.1 Example: Calculating and comparing

# Calculate
result <- (23+24)*11/(18+15)*5
result

# Functions
log(2) 
cos(2)

# Comparison
x <- -3:3
x

# Are the elements of x equal to 0?
x == 0

# greater than 0?
x > 0

# less than 0?
x < 0

# greater than or equal to 0?
x >= 0

# less than or equal to 0?
x <= 0

# not equal to 0?
x != 0

# greater than -1 but less than 1
x > -1 & x < 1

# greater than 1 and less than -1
x > 1 & x < -1

# greater than 1 or less than -1
x > 1 | x < -1

5.2 Exercise: Calculating and comparing

calculate the following terms in R
1. $((3 + 4 - 5) - 9)^{2}$
2. $log(1)$
check the following comparisons:
1. $5 = 7$
2. $\sqrt{3} \neq cos(17)$

5.3 Solution: Calculating and comparing

6 Class / data types

class(): Reveals the class of an object
numeric
logical (TRUE/FALSE)
Character/String (The Beatles)
Some data types can be converted, e.g. as.numeric() or as.character()
List, e.g. list(1, "Hello", TRUE)
Data frame: “list” of vectors of the same length
Factors: represent categorical data. These are stored as numerical values but are linked to a value label

6.1 Overview of data types

# Integer vector
x <- c(1, 2, 3)
class(x)
x

# Logical vector
x <- -3:3
y <- x >= 0
y
class(y)

# String/character vector
x <- c("a", "b", "c")
class(x)
x

# list
list <- list(a= c(4:8), b = c("a", "b", "c"), c = c(TRUE, FALSE))
class(list)
list

# factors
sex <- c(0, 0, 1, 1)
factor(sex, labels=c("man", "woman"))

# Functions: e.g. cos(); mean()
class(mean)
mean

6.2 Special values

Inf and -Inf: Positive and negative infinite
NaN: “Not a number”, e.g. 0/0.
NA: missing value (Missing)

# Important note on missing values:
x <- c(1, 2, NA, 4)

#wrong:
x == NA
x == "NA"

#correct:
is.na(x)

6.3 Data Frames

Data frames are the typical format for data sets
…it is a list of vectors of the same length see Wickham
…resembles matrices but the columns can contain different data types
data.frame(): creates a data frame
as.data.frame(): converts to a data frame
order(): sorts data
summary() and str(): overview of data frames
head() and tail(): inspect first/last lines
names(): show column names
object$var1: directly accesses the column var1 in the data frame object
na.omit(): Row-by-row exclusion of missing values, i.e. rows that contain at least 1 missing value

6.3.1 Example: Data Frames

# Prepared data are often data frames.
richtungswechsel <- read.csv("S:/MA1082973/_FHNW/BA472/data sets/Richtungswechsel/Richtungswechsel_anonymized data set.csv")

class(richtungswechsel)

# you can also easily build one yourself
beruf <- c("Lehrerin", "Verkäufer", "Pilotin")
nation <- c("CH", "DE", "IT")
id <- 1:3
df <- data.frame(id, beruf, nation)
df

# Addressing rows and column positions
df$nation
df[, "nation"]
df[3, "beruf"]
df[3, 3]

6.4 Access to different data areas

# How can I access specific elements of a vector directly?
x <- seq(2, 200, 2)
x
x[1] # first element of x
x[1:10] # the first 10 elements of x

# For two-dimensional objects, both rows and columns can be accessed:
# load Richtungswechsel data
richtungswechsel <- read.csv("S:/MA1082973/_FHNW/BA472/data sets/Richtungswechsel/Richtungswechsel_anonymized data set.csv")

richtungswechsel[1:2, c(3, 6)] # reads: "first to second row, third and sixth column"

# Eselsbrücke: Zeilen zuerst, Spalten später.


# Apart from the position, the name of a column (or row) can also be used for referencing:
richtungswechsel[, c("Geschlecht", "Staatsang.", "europe")]

# or by a condition:
richtungswechsel[richtungswechsel$Bezugsdauer > 4, ]

6.4.1 Exercise

read the data set Richtungswechsel into R.
display the tenth and twelfth rows of the data set.
display the columns `Bezugsdauer and Bildungsstand for all persons aged between 25 and 40.

6.4.2 Solution

7 Data management

7.1 Tidy vs. Messy Data

7.1.1 General

some conventions for the clean presentation/storage of data (Hadley Wickham)
see http://vita.had.co.nz/papers/tidy-data.pdf and http://tidyverse.org/
Quintessence:
- once data is clean (tidy), analysis tools (plotting, model fitting) can also work cleanly and without additional effort (e.g. ggplot2, lm/glm)
- Cases (observation units) in rows, variables (observation dimensions) in columns
Data is messy if
- Columns are not labeled
- A column contains more than one variable
- variables also appear in rows instead of columns
- different observation units are in the same table
Tools to clean data (small selection):
- dplyr / data.table
- melt() dcast() from reshape2
- str_replace(), str_sub() from the stringr package
- tolower()
- some more in package tidyr
- also useful: recode() from John Fox (package car)

7.1.2 Example: Tidy vs Messy Data

# Wetterdaten
weather <- read.table("https://raw.githubusercontent.com/justmarkham/tidy-data/master/data/weather.txt", header=TRUE)
head(weather) # the variables are in rows and columns 

# reshape the data (melt) and delete mssings values
library(reshape2) # for melt()/dcast()
weather1 <- melt(weather, id=c("id", "year", "month", "element"), na.rm=TRUE)
head(weather1)

# clean column for "day"
library(stringr)    # for str_replace(), str_sub()
weather1$day <- as.integer(str_replace(weather1$variable, "d", ""))

# we do not need the "variable" column
weather1$variable <- NULL

# The element column contains two different variables tmin and tmax. 
# These should be in two columns:
weather1$element <- tolower(weather1$element) # lowercase letters
weather.tidy <- dcast(weather1, ... ~ element) # reshapen to two columns
head(weather.tidy)

# the date can also be displayed in a column as a real date:
weather.tidy$date <- as.Date(paste(weather.tidy$year, 
                                   weather.tidy$month, 
                                   weather.tidy$day, sep="-"))
weather.tidy[, c("year", "month", "day")] <- NULL
head(weather.tidy)

7.1.3 Exercise

Read the data set SHP into R. You can use this command: read_sav(“your workingdirectory/SHPLONG_P_USER.sav”) in haven package.
Look at the data structure and the variables of the data set. Also use functions such as: summary(dataset\$var), head(dataset), names(dataset)

7.1.4 Solution

8 Descriptive statistics

8.1 Contingency tables

table(x): one-dimensional contingency table
table(x,y): two-dimensional contingency table
prop.table(table(x)): relative frequency

8.1.1 Example: Contingency tables

# Let's define a new variable AGE that contains the age (in years) of ten people.
age <- c(76, 54, 38, 96, 32, 76, 81, 81, 50, 75)

# Let's take hosp to be a variable that contains the information if the same ten persons have been hospitalized in the last six months (1= yes, 0 = no)
hosp <- c(1, 0, 0, 0, 0, 1, 0, 0, 1, 0)

# Tabelle
table(age)
table(age, hosp)

# Tabelle in Prozent
100*prop.table(table(age))
100*prop.table(table(age, hosp))

8.2 Univariate statistics (1 variable)

mean(x): Mean
sd(x): Standard deviation
var(x): Variance
median(x): Median
min(x): minimum
max(x): Maximum

# Mean
mean(age)
sd(age)

# Median
median(age) 
sort(age)

# Funktion summary
summary(age)

8.3 Package dplyr

8.3.1 General

package dplyr by Hadley Wickham/Romain Francois offers a toolset for data preparation
See the dplyr vignette and the Data Wrangling Cheat Sheet for a very good overview
- filter(): selects a subset of rows (see also slice())
- arrange(): sorts
- select(): selects columns
- mutate(): creates new columns
- summarize(): aggregates (collapses) data to individual data points
- distinct(): removes duplicate values
- group_by(): defines subgroups in the data so that mutate() and summarize() can be applied separately per group.
- dplyr can be used very well together with so-called piping, i.e. the data object is passed from function to function by %>%, which makes the code much easier to read and more compact.

8.3.2 Example: Package dplyr

# load data
library(dplyr)
shp2022

# filter by 1st nationality not Switzerland and persons up to 65 years old  
# select columns
shp2022a <- shp2022 %>% 
  filter(NAT_1_ != 8100, AGE < 66) %>% 
  select(c("AGE", "SEX", "NAT_1_", "EDUCAT"))
  

# create new variable "Tertiary education"
shp2022a <- shp2022a %>%
  mutate(EduTertiary = EDUCAT == 10)

# Count by gender and with a tertiary education (TRUE/FALSE)
tab <- shp2022a %>%
  group_by(SEX, EduTertiary) %>%
  summarise(n=n()) %>%
  arrange(SEX, EduTertiary) %>%
  na.omit()

8.3.3 Exercise: Package dplyr

Read the SHP data into RStudio.
Restrict the data set for the year 2022 to people who are 25 years or older. Familiarize yourself a little with the data (e.g. head(), summary(), table()).
Look at the variables with the information on age (variable AGE), gender (variable SEX), years of education (variable EDYEAR) and first nationality (NAT_1_).
Create a crosstab with the variables EDYEAR and SEX.
calculate mean and standard deviation for the variable age for men and women.

8.3.4 Solution: Package dplyr

8.4 Bivariate statistics (2 variables)

cov(): covariance
cor(): Correlation
cor(x,y,method="spearman"): Rank correlation
?cor: more information in the helpfile
chisq.test(): Chi-square test
t.test(): t-test

9 Data visualization

Why is data visualization important?
Data exploration vs. data presentation
Simple diagrams
Histograms
Scatter charts
Bar charts

9.1 Visualization with ggplot2

ggplot2 is the most important package to create visualizations in R
see also cheatsheet: https://rstudio.github.io/cheatsheets/data-visualization.pdf

Hadley Wickham’s ggplot2 package has developed into a particularly useful alternative to plot() over the last few years. Especially complicated plots are easier to implement with ggplot, visually appealing and the code is easily accessible. Most important basic structures:

Data that we want to visualize
Geometries to define the shapes we want to use for visualization (e.g. a scatter plot, line chart, bar chart)
Modify aesthetics to convey different meanings (e.g. colors, size, thickness of a line)
Define mappings between geometries and aesthetics (e.g. how big should the data points be)

9.1.1 Example: ggplot2

library(ggplot2)
library(dplyr)
library(haven)

# set working directory
setwd("S:/MA1082973/_FHNW/BA472")
# load data
shp <- read_sav("data sets/SHP/SHPLONG_P_USER.sav")

# Scatterplot (einfach)
shp2022 <- shp %>%
  filter(YEAR==2022) %>% filter(AGE > 24)

ggplot(data=shp2022, aes(x = PC46, y =PC45)) +
  geom_point() +
  labs(title = "Streudiagramm",
       x = "Weight in kg",
       y = "Height in cm")

#
ggplot(data=shp2022, aes(x = PC46, y =PC45, color=factor(SEX))) +
  geom_point() +
  labs(title = "Streudiagramm",
       x = "Weight in kg",
       y = "Height in cm",
       color = "Geschlecht") +
  scale_color_manual(values=c("lightgreen", "darkviolet", "red"),
                    labels=c("Männlich", "Weiblich", "Andere"))

# Erstelle ein Säulendiagramm
# Entfernen der NA-Werte für die Variable SEX
shp2022 <- shp2022 %>% filter(!is.na(SEX)) %>% filter(!is.na(PC44))

ggplot(shp2022, aes(x=factor(PC44), y=..count.., fill=factor(SEX))) +
  geom_bar(stat="count", position="dodge") +
  labs(x="Zufriedenheit mit dem Leben", y="Anzahl", fill="Geschlecht") +
  #Beschriftung für Legende
  scale_fill_manual(values=c("lightgreen", "violet", "red"),
                  labels=c("Männlich", "Weiblich", "Andere")) +
  ggtitle("Zufriedenheit mit dem Leben nach Geschlecht")

9.1.2 Exercise: Grafiken mit ggplot2

Load the SHP data.
Create a histogram for income (variable: IPTOTN) for the year 2022. give the graph a title. Tip: Add a labs() argument to the chart.
Create a boxplot showing the distribution of income by educational level (variable: ISCED). In order for the boxplots to be created separately for each education level, you must first save the education variable as a factor (the general form is: data$variable <- as.factor(data$variable)). Also restrict the income (exclude outliers, e.g. all incomes over 200000).

9.1.3 Solution: Grafiken mit ggplot2

10 Measuring the effects of social work

10.2 What is an effect and what not?

Effect = difference in the result with influencing variable versus without influencing variable (= counterfactual situation)
What is the counterfactual situation?
- The story of Tom and Eva
  - “Eva was a saleswoman in a supermarket in Lucerne, who came from a poor family. She dropped out of school after the ninth grade to financially support her parents. In 2005, she met Tom, a mechanic from St. Gallen who worked in a workshop. They fell in love and got married in 2007. They had two children: a son named Max in 2008 and a daughter named Zoe in 2010.
    
    Eva and Tom had a loving marriage but also their problems. They had to deal with the low income, high rent, and high cost of living. They sometimes argued, but they always talked it out and found a solution. They valued each other and supported each other’s wishes.
    
    However, in 2017, the relationship between Eva and Tom broke down. Eva discovered that Tom was having an affair with a customer he had met in his workshop. She was angry and hurt and confronted him about it. Tom admitted his infidelity and told her that he no longer loved her. He wanted a divorce and to move out.
    
    Eva was devastated and agreed to the divorce. They had to arrange custody for their children, who suffered from their parents’ separation. They decided on joint custody, with the children alternating between living with each parent. They also had to divide the assets they had accumulated during their marriage. They sold their apartment in Lucerne and split the proceeds.
    
    Today, Eva and Tom are divorced and live separately. They have both continued their careers but with less success than before. Eva is still a saleswoman in a small supermarket but has no prospects for promotion or salary increase. Tom is still a mechanic in a small workshop but faces a lot of competition from younger colleagues. They earn less money than before and have trouble paying their bills. They also have less time for themselves and for their children, who are unhappy and insecure. Since 2020, Eva has been supported by social welfare.”
- The counterfactual version
  - “Eva was a saleswoman in a supermarket in Lucerne, who came from a poor family. She dropped out of school after the ninth grade to financially support her parents. In 2005, she met Tom, a mechanic from St. Gallen who worked in a workshop. They fell in love and got married in 2007. They had two children: a son named Max in 2008 and a daughter named Zoe in 2010.
    
    Eva and Tom had a loving marriage but also their problems. They had to deal with the low income, high rent, and high cost of living. They sometimes argued, but they always talked it out and found a solution. They valued each other and supported each other’s wishes.
    
    In 2017, Eva found a job as a cashier in a larger supermarket, while Tom continued to work in the workshop. They earned more money and were able to buy their own apartment. They also had more time for themselves and for their children, who were happy and healthy. Today, Eva and Tom are still happily married and looking forward to the future. They plan to take a trip to Spain soon to enjoy the sun. They also hope to become grandparents one day, as their son Max has a lovely girlfriend whom he has been dating for six months. Their daughter Zoe is still at school, but she already has many talents and interests that she wants to pursue.”

# Create two vectors with the years and the estimates for Version 1
year <- c(2004:2024)
est <- c(4, 5, 6, 7, 8, 7, 8, 8, 7, 7, 8, 7, 8, 4, 3, 3, 2, 2, 2, 2,3)

# Create a table with the vectors as columns
tab1 <- data.frame(year, est, type = "Life Satisfaction")
save <- est

year <- c(2004:2024)
est <- c(4, 5, 6, 7, 8, 7, 8, 8, 7, 7, 8, 7, 8, 9, 9, 9, 8, 9, 9, 9,8)

# Create a table with the vectors as columns
tab2 <- data.frame(year, est, type = "Life Satisfaction")

tab3 <- data.frame(year, est = save - est, type = "Effect of Divorce", version = "Effect of Divorce")

library(ggplot2)

# Add a new column indicating the version
tab1$version <- "Real Situation"
tab2$version <- "Counterfactual Scenario"

# Combine the two tables
tab <- rbind(tab1, tab2, tab3)

# Create a line plot with ggplot
ggplot(tab, aes(x = year, y = est, color = version)) +  
  geom_line() +  
  facet_grid(type ~ ., scales = "free_y") + 
  labs(
    title = "The Effect of Eva's Divorce", 
    subtitle = "Eva's Life Satisfaction in the Actual and Counterfactual World", 
    x = "Year", 
    color = "Effect", 
    y = "Life Satisfaction resp.\nEffect of Divorce on Life Satisfaction"
  ) +  
  geom_vline(xintercept = 2016.5)

Example of measuring the effects of social work

This is not a measure of effect:
- Federal Council guidelines for the labor market integration of persons with protection status S for cantons: 40% should have a job until the end of 2024
- Why does a canton (not) reaching the goal of 40% say much about the effects of its measures?

10.4 Analyzing social work experiments in R

10.4.1 Example

#We will analyze the effect of "Richtungswechsel" on long-term recipients' vitality. https://journals.sagepub.com/doi/full/10.1177/10497315241232120

#Download the data: with this command you can download files if you have the direct link to the file
download.file("https://drive.switch.ch/index.php/s/zpjX3z1frPOQKqr/download", "Richtungswechsel.R",mode = "wb")

#Because the file is in R-Data-Format (.R) we can load it directly
load("Richtungswechsel.R")

#To prepare the data for analysis and to analyze the data we will install/load the package "dplyr"
#install.packages("dplyr")
library(dplyr)

#Let's have a look at the Data
View(data.anonym)

#Are there missing values?

summary(data.anonym)

#Vit1 contains measures of vitality before Richtungswechsel, vit2 contains measures of vitality after Richtungswechsel

#We will give the data a nicer name
richtungswechsel <- data.anonym%>%
  filter(!is.na(vit2))%>%#We remove missing values
  mutate(vitality.change=vit2-vit1,#Here we calculate a new column measuring the change in vitality

         Group=ifelse(INT1==1,"Intervention group","Comparison group")) #Here ge give the variable INT1 (Group membership) a nicer, more telling name and labels
#Were there more positive changes in the intervention group?

#Here we check whether the two groups are comparable in terms of 

richtungswechsel%>%
  group_by(Group)%>%#We tell R to do all calculations groupwise 
  summarise(Mean.Age=mean(age),
            Mean.Gender=mean(Geschlecht),
            Mean.Bezugsdauer=mean(Bezugsdauer))#We tell R to summarise all variabels in the data

#Here we calculate the effect of the intervetion
richtungswechsel%>%
  group_by(Group)%>%
  summarise(Mean.Change=mean(vitality.change),
            Mean.Vit1=mean(vit1,na.rm = T),
            Mean.Vit2=mean(vit2,na.rm=T)) #We tell R to calculate the mean value of the change in vitality. We must tell it to remove missing values.

#Was the effect stronger for men or for women?

richtungswechsel%>%
  group_by(Group,Geschlecht=ifelse(Geschlecht==1,"Women","Men"))%>%#We tell R to do all calculations groupwise 
  summarise(Mean.Change=mean(vitality.change,na.rm = T)) #We tell R to calculate the mean value of the change in vitality. We must tell it to remove missing values.

10.4.2 Excercise

Download data from Fokus Arbeit. You can find them here.
Inspect data with respect to missing values and remove observations with missing values
Check the distribution of gender, duration of benefit receipt, age and vitality before the intervention in the intervention and the comparison group. Are the two groups comparable?
Calculate a new variable measuring the change in vitality before versus after the intervention.
Answer the following questions
- Are social assistance clients in Biel more vital than those participating in Richtungswechsel?
- Did Fokus Arbeit increase vitality? Did it more so than in Richtungswechsel?
- Were the effects different by age, gender, duration of benefit receipt?

10.5 Important references

11 Prediction in social work

Evtl. 1/2 Tag oder 3/4 Tag

Risk prediction in SW
Examples in practice
Black et al. - 2003 - Is the Threat of Reemployment Services More Effect.pdfBlack et al. - 2003 - Is the Threat of Reemployment Services More Effect.pdf
Gilholm et al. - 2023 - Machine learning to predict poor school performanc.pdf
Kleinberg et al. - 2018 - Human Decisions and Machine Predictions.pdfKleinberg et al. - 2018 - Human Decisions and Machine Predictions.pdf
Rittenhouse et al. - 2023 - Algorithms, Humans and Racial Disparities in Child.pdfRittenhouse et al. - 2023 - Algorithms, Humans and Racial Disparities in Child.pdf
Stevenson and Doleac - 2022 - Algorithmic Risk Assessment in the Hands of Humans.pdfStevenson and Doleac - 2022 - Algorithmic Risk Assessment in the Hands of Humans.pdf
Tennakoon et al. - 2023 - Using electronic health record data to predict fut.pdfTennakoon et al. - 2023 - Using electronic health record data to predict fut.pdf
Toros and Flaming - 2018 - Prioritizing Homeless Assistance Using Predictive .pdfToros and Flaming - 2018 - Prioritizing Homeless Assistance Using Predictive .pdf
OLS, (Machine Learning)
Organisation: Gruppenformierung

11.1 Content

Content is structured to alternate between technical aspects (basics, hands-on exercises) and their relation to social work and practice.

#BFH Struktur

Preparation (Samin erstellt Begrüssungsemail)
- Begrüssungsnachricht: Download r/rstudio, videolink, aufgabe
- Some video, introduction course (Samin sucht video)
- Find examples of how data science can help social work: sie sollen ein Forumsbeitrag erstellen
Introduction (Dorian/Samin, 21.5.)
- What is data science and how can it help social work? What are the limits? (Slide 4 bis 7)
- Data that are relevant for social work
  - Datafication:
    - data collected for research/official statistics,
    - found data (uebung: studierende sollen selber überlegen, wo found data entstehen)
- Crash course in R and R Studio: AI supported coding (ChatGPT-4 / Copilot), warum R, R as a calculator/objects/for loops (kurz), reading in data, überblick über datensatz (summary, nrow,…), dplyr, filter/select, mutate, rbind/merge, reshaping (?), umgang mit textdaten (?), saving
Descriptive analyses (Samin, 27.5.)
Prediction of risks (Dorian, 28.5)
Evaluation/Causal design/Effects of social work (Dorian, 3.6.)
- Evtl. 1/2 Tag oder 3/4 Tag
- Randomized control trials
- Natural experiments
Case studies mit Coaching (Dorian, 4.6.)
Beispieldaten
- Beispieldaten oben einbauen
- Richtungswechsel
- Swiss Household Panel (Samin lädt herunter)
- Data dashboard Wohlen; extension or data analysis of dependency duration (Dorian fragt nach; wir sagen nicht von welcher Gemeinde, Vertragliche Abmachung)
- Jugendarbeit Burgdorf (Dorian schreibt Mani)
- Fokus Arbeit (?): Wenn andere Daten nicht verfügbar sind
- ESS (Samin lädt herunter) ..Daten die relevant sind für die SA
- Open data/BFS/…Daten die relevant sind für die SA
Struktur KNW
- 5000-7000 Zeichen
- Beschreibung Fragestellung, Daten, Code, Resultate (Tabellen/Grafik), Interpretation (Interpretation der Ergebnisse, Limitation der Daten/Analysen/Schlussfolgerungen)
Vorbereitung von Tutorials/Videos/Aufträge etc. im Falle von Krankheit…https://www.theanalysisfactor.com/resources/

#FHNW Struktur - Introduction (Samin, 3.6.) - Organisation: Gruppenformierung - What is data science and how can it help social work? What are the limits? (Slide 4 bis 7) - Data that are relevant for social work - Datafication:
- data collected for research/official statistics, - found data (uebung: studierende sollen selber überlegen, wo found data entstehen) - excercise: measuring social work outcome dimensions with found data (give example) - risks? - Crash course in R and R Studio: AI supported coding (GPT-4/Bing, Copilot), warum R, R as a calculator/objects (kurz), reading in data, überblick über datensatz (summary, nrow,…), dplyr, filter/select, mutate, merging, reshaping, umgang mit textdaten, saving - Descriptive analyses (Samin, 4.6.) - Study designs: descriptive, prediction, evaluation/causal design - Descriptive: Monitoring, Probleme identifizieren, Überblick gewinnen, Entwicklungen beschreiben - Prediction: (ungewünschtes) Vorhersagen - Evaluation/Causal design: Wirkungen messen, Lösungen identifizieren - Distributional characteristics/univariate statistics: Frequencies, mean, median, modal value, variance, interquartile range, range of values. - Correlations/bi-/multivariate statistics: correlation measures, associations and group comparisons using regression or other statistical techniques. - Einfach halten: Verweis auf Methodenberatungswebseite UZH - Uncertainty: statistical significance, p-value and confidence intervals. - Einfach und kurz - Results communication: tables with quick_docx huxtable, ggplot2, (shiny; erwähnen) - Prediction of risks, Evaluation/Causal design/Effects of social work (Dorian, 5.6) - Case study work with coaching (Dorian, 6.6) - Presentation

11.1.1 Technical Part

Study Designs
- Description, prediction, causal inference/evaluation design.
Measurement Theory
Survey Data
- (Note: This might not be necessary since it is covered in standard curricula.)
Crash Course: Interface of R/Python
- Basic functions, reading in and preparing administrative data, survey data, or data from organisations using R/Python.
- Doing simple calculations, creating descriptive tables and simple graphs.
Descriptive Statistics:
- Frequencies.
- Mean, median, modal value.
- Variance, interquartile range, range of values.
- Correlation measures.
- Associations and group comparisons using regression or other statistical techniques.
Machine Learning Applications

Data skills for social work professionals

Dorian Kessler and Samin Sepahniya

May 2024